The key challenge that existing predictive analytics software has not been able to solve is how to extract knowledge from data quickly and put it into the hands of data owners to make better, more informed decisions. Separating decision-making from application process logic is often beneficial, hence designing adaptive predictive models based on Software as a Service (SaaS) paradigm makes such technology more accessible.

Leveraging Prediction.IO machine learning stack built on top of Apache Spark, HBase, Spray, and Elastic Search, this study addresses the process of constructing and components of new prediction engines that are consumed over web API. The main challenges addressed in this study are concept drift, varying data type and distribution, and data stream mining.